Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors

Abstract

Existing forest fire risk prediction methods often focus on single-element analysis, which makes it difficult to effectively capture the underlying mechanisms of the “fuel-climate” interaction. This paper proposes a physically aligned prediction framework named FWI-MSNet. Using 18 years of synchronized observation data from the Huitong Ecological Station in China, the framework constructs a multi-scale feature system, selecting 21 physically relevant key features, including core indicators of the Forest Fire Weather Index (FWI). A parallel multiscale 1-Dimensional Convolutional Neural Network (1D-CNN) extracts dynamic features across multiple temporal scales (from daily to seasonal). Subsequently, a synergistic mechanism integrating Gated Recurrent Units (GRU) and Transformer achieves deep integration of temporal evolution and global correlation. Experimental results demonstrate that compared to seven baseline models (including XGBoost, LSTM, CNN, and four ablation variants), the mean of RMSE, MAE, and MAPE decreases by 56.1%, and R2 improves from 0.223 (baseline average) to 0.9251. Furthermore, it accurately captures the trajectory of fire risk evolution in the 2020 catastrophic forest fires in Australia. This study provides a valuable reference for forest fire risk prediction.